Commercial remote sensing companies build and launch satellites that collect images of the Earth (sometimes referred to herein as “Earth Imaging” satellites). Often, a company will operate multiple satellites that collect digital images in a variety of spectral bands or channels. Sensors that collect radiation in a small, distinct number of separate spectral channels with narrow spectral band passes are called multispectral instruments. In layman's terms, such imagery is referred to as color imagery. Sensors that collect radiation in a single spectral channel with a broadly defined spectral band pass are referred to as panchromatic or sometimes Electro-Optical (EO) imagery. In layman's terms, such imagery is referred to as “black and white.”
The images are collected by the satellites, down-linked to a ground station, and processed to create geographically-referenced digital imagery. The resulting imagery is sold to customers who desire the imagery for a variety of purposes, from commercial web applications and sophisticated mapping applications, to image mining, classification and understanding. In many cases, the customer may be interested in non-cloudy pixels in the imagery, since the presence of clouds obscures the features of interest on the ground. Thus, cloudy pixels are often viewed as a defect in the imagery.
In practice, often only part of a given image will be obscured by clouds while another part of the image may be completely cloud-free. Thus, the cloud-free part of the image (e.g., cloud-free pixels) may be used to fulfill all or part of a customer's order and represents commercially valuable pixels. In contrast, pixels obscured by clouds, or cloudy pixels, are of limited utility for geographically-referenced digital imagery. In this situation, the remote sensing company spends considerable time and money either delineating the cloud locations in the imagery manually, investing in a system to detect clouds automatically in imagery, or performing a combination of both automated and manual processing.
To date, known functional cloud detection techniques focus on exploiting spectral characteristics of the collected imagery to detect the presence of cloudy pixels. In many cases, the techniques require spectral measurements in different spectral channels, or bands, of a variety of wavelengths to operate, and have a relatively narrow associated bandpass for the spectral channels. Current known techniques are now discussed in more detail.
In U.S. Pat. No. 6,990,410, entitled “Cloud Cover Assessment: VNIR-SWIR” A. Boright describes a technique for classifying cloudy pixels using spectral information from a sensor designed to measure reflected solar energy in the 1.88 micrometer and 1.38 micrometer wavelength spectral bands. The following general process is utilized in the '410 patent:                a first comparison of the cirrus band reflectance with a cirrus band reflectance threshold is performed;        a second comparison of a normalized difference snow index with a threshold is performed;        a third comparison of a “D” variable with a threshold is performed;        a fourth comparison of a “D” spatial variability index with a threshold is performed;        a fifth comparison of the ratio of near infrared to SWIR ratio with a threshold that may be determined empirically is performed.        
In various embodiments disclosed in the '410 patent, the cirrus band wavelengths are approximately 1.88 micrometers and 1.38 micrometers. The '410 patent defines a normalized difference snow index (NDSI) as (ρgreen−ρSWIR1)/(ρgreen+ρSWIR1), the D variable is defined as NDVI0.6/(ρred)2 and a normalized difference vegetation index (NDVI) as =(ρred−ρNIR)/(ρred+ρNIR). The spatial variability index is defined as Dm−Dc where Dm is the mean of at least a 3×3 matrix of data points, where Dc is the central pixel in the matrix. Thus, the spectral classification technique of several embodiments disclosed in the '410 patent may be said to rely on the presence of spectral bands at both 1.88 micrometers and 1.38 micrometers and a red spectral band for the technique to work.
In U.S. Pat. No. 5,612,901, entitled “Apparatus and Method For Cloud Masking”, S. Gallegos describes a method and apparatus for detecting and masking cloud pixels over a body of water for an image collected by the AHVRR instrument. The AVHRR instrument operates at 1 km resolution. The method disclosed in the '901 patent generally operates as follows:                Using the well-known gray level co-occurrence (GLC) metric to compute a “cluster shade image” from the image measured by collecting spectral information in channel 1, operating at 585-685 nanometers (nm) for daytime collections. For nighttime collections channels 3, 4, and 5 operating at 3,575-3,983 nm, 10,362-11,299 nm, and 11,450-12,424 nm respectively are used to generate the cluster shade image.        Generating edges from the cluster shade image using a zero crossing test, in which zero crossings are identified at the sites of neighboring pixels shows cluster shade values are opposite in sign. This is equivalent to a segmentation process applied to the cluster shade image.        Closed polygons are formed from the edge images. Smaller polygons are rejected as noise and are not included in the solution.        Cloudy and cloud free polygons are separated on the basis of cloudy areas having a higher albedo and lower temperature than the underlying water body.        
The algorithm in the '901 patent is a spectral classification method for clouds and relies on measurements in the following spectral bands:                Channel 1: 585 nm-685 nm        Channel 3: 3,575-3,983 nm        Channel 4: 10,362-11,299 nm        Channel 5: 11,450-12,424 nm        
As such, embodiments as described in the '901 patent are limited to the AVHRR sensor (or a sensor which performs measurements in the same spectral bands as listed above). Further, embodiments of the method of the '901 patent are based on “the assumption that clouds are colder and brighter than the surrounding ocean.” Higher albedo than the underlying land cover is only true when the underlying land cover is large water bodies, like oceans. Thus, the method of the '901 patent is not necessarily accurate over small bodies of water or land.
In U.S. Pat. No. 7,480,052, entitled “Opaque Cloud Detection”, J. Roskovensky discloses a method for detecting opaque clouds in digital images. This method operates by comparing the spectral information contained in several different spectral bands defined as follows:                Band Number 1: 400 nm-460 nm        Band Number 2: 630 nm-690 nm        Band Number 3: 860 nm-890 nm        
The input imagery is converted to top-of-atmosphere reflectance (TOA), which is well-known. Embodiments disclosed in the '052 patent employ three individual cloud tests defined below:                The Normalized Blue-Infrared Reflectance difference (NBIRD) is computed. The NBIRD ratio is defined as (R3−R1)/(R3+R1), where R3 is the reflectance in band number 3, and R1 is the reflectance in band number 1.        The Reflectance Ratio (RR) is computed. The RR is defined as R3/R2 where R2 is the reflectance in band 2.        The Visible Reflectance (R2) is computed, and is defined as simply the band 2 reflectance.        
In an embodiment, certain thresholds for each ratio are established that are either empirically determined from the data or defined from theoretical arguments. Testing a given pixel for presence of cloud comprises computing the quantities defined above for the pixel and testing the results against pre-defined thresholds. In an embodiment, the method uses all three cloud tests and opaque clouds tend to pass all three cloud tests. In addition, the '052 patent defines cloud probability values for each test that are based on a linear scaling of the measured result between the limits of the threshold. The three individual cloud probabilities are combined into a cloud probability (CP) measure which quantifies the overall probability that the pixel is a cloud. The cloud probability is defined as sqrt((sqrt(CPRR* CPNBIRD)*CPR2)) where CPRR is the cloud probability from the RR test, CPNBIRD is the cloud probability from the NBIRD test, and CPR2 is the cloud probability from the R2 test.
Thus, the method disclosed in '052 patent is a spectral classification method that may be said to rely on the presence of at least three spectral bands in the visible wavelength range from 400 nm to 890 nm. Three spectral quantities are performed for each pixel in the image and the results are compared to pre-defined ratios designed to indicate the presence of a cloud. Mention is made in the '052 patent of applying its method specifically to sensors which collect data at a spatial resolution of 250 m and specific reference is made to the MODIS sensor. The '052 patent therefore suggests its method could not be successfully applied to high resolution imaging sensors, such as those used in modern high resolution imaging satellites.